Many organizations collect historical data about every transaction that every customer performs with that organization. Such historical transactional data is useful in various one-to-one marketing applications, such as, e.g., shopping assistant application and dynamic Web site content presentation. A number of problems have been encountered in these marketing applications. One such problem relates to the creation of highly pertinent and comprehensible individual user profiles that are derived from the historical transactional data. In addition, it is also important to have the ability to utilize these user profiles when the marketing application obtains a current status of the user. If the user profiles are generated in a highly relevant and comprehensible manner with respect to a specific user, the applications would be able to understand that user's needs better and more efficiently serve that user.
There are two basic types of user profiles that can be generated—a “static” profile and a “dynamic” profile. The static profile contains all of the factual information of the user including, for example, demographic data (e.g., age, sex, address), psychographic data (e.g., personality traits and habits), purchasing preferences (e.g., what does the user purchase in an average week), etc. Static profiles are generated using conventional methods that are known to those of ordinary skill in the art.
Dynamic profiling information includes specific rules describing the user's behavior. For example, such rules may include: “whenever user X travels to France, user X often buys expensive wines in Paris” or “when user Y shops on a weekend and did not buy any groceries for at least 3 days, user Y usually purchases a large amount of groceries.” These rules can be generated with transactional data for each user using various rule generation methods that are generally known to those of ordinary skill in the art. For example, one such conventional rule generation method is implemented in a rule learning system which generates behavior rules for individual customers. (See T. Fawcett et al., “Combining Data Mining and Machine Learning for Effective User Profiling”, Proceedings of the KDD'96 Conference, 1996, pp. 8-13).
In order to obtain an extensive understanding of the user, it is desirable to build both static and dynamic profiles for that user. Although the generation of static profiles is generally straight-forward, generating dynamic profiles for a large number of users may present potential problems. Many transactional systems (e.g., airline reservations systems, credit card transactional systems and/or Web site management systems) generate a various number of transactions for each user. For example, some systems and/or applications may only generate a dozen transactions per each user, which may not be enough to construct a statistically significant and reliable set of rules for a specific user. Even if there are enough transactions to construct a statistically significant set of rules, these rules should still be verified for their pertinence to the user. Since there can be a large number of users, and since the rules generated for each user may not be reliable, there is a problem of verifying a large set of generated rules for the users. For example, in a typical system facilitating 5 million users and providing about 100 rules per user, approximately 500 million rules would have to be either stored or processed. Generally, many of these rules are either not useful or insignificant. Thus, due to the amount of these generated rules, a rule validation process becomes considerably complicated. Furthermore, checking the usefulness of these rules “by hand” becomes practically impossible.
Conventional systems have not successfully provided detailed solutions to constructing reliable dynamic profiles for the users. One such system (described in T. Fawcett et al., “Combining Data Mining and Machine Learning for Effective User Profiling”, Proceedings of the KDD'96 Conference, 1996) provides a limited generation of user's dynamic profiles. However, this conventional system does not provide a comprehensive method and system for analyzing a large number of dynamic rules, and thus does not provide adequate assistance for the user.